scholarly journals EMPIRICAL ANALYSIS OF DISEASE BURDEN AND WEALTH CREATION IN NIGERIA

2021 ◽  
pp. 71-81
Author(s):  
Festusdading
Keyword(s):  
Time Series ◽  
Life Expectancy ◽  
Empirical Analysis ◽  
Disease Burden ◽  
Time Series Data ◽  
Series Data ◽  
Wealth Creation ◽  
Life Expectancy At Birth ◽  
Life Years ◽  
The Impact

The study investigates the impact of disease burden on wealth creation in Nigeria using annual time series data ranging from 2006-2018. A pre test was conducted to ascertain the stationarity of the data for it is a common knowledge that most time series data are not stationary. The case of this is not an exception to the assertion. Some of the variables were stationary at levels while others were stationary after first difference, that is I(0) and I(1) respectively. The different levels of integration of the data warrant the use of Autoregressive Distributed Lag (ARDL) model as a methodology for the analysis. The theoretical framework was based on the Disability Life Years (DALY) of Murray (1996) which says that the burden of disease is arrived at by the number of years a person loses as a result of dying early due to ailment and the number of years of life a person lives with disability caused by the disease. Diagnostic test was conducted using Breusch-Godfrey Serial Correlation Lagrange Multiplier Test to ascertain whether there is autocorrelation among the variables but the result shows that there is no trace of autocorrelation among variables. The long run empirical analysis revealed that wealth creation has impact on disease burden in Nigeria. This can conversely infer that as the source of livelihood increase, the disease burden on the population is reduced because people will have income to attract health consideration which invariably will reduce the chances of suffering from diseases. The life expectancy at birth has more and significant impact on disease burden than population and even wealth creation. It is therefore recommended among other recommendations that measures that will lead to wealth creation should be harnessed as this will greatly reduce the burden of diseases in Nigeria which will invariably increase life expectancy at birth.

Impact of Globalization on Economic Growth in Vietnam: An Empirical Analysis

2018 ◽  
pp. 32-47 ◽  
Author(s):  
Dat Tho Tran ◽  
Van Thi Cam Nguyen
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Foreign Direct Investment ◽  
Exchange Rate ◽  
Empirical Analysis ◽  
Direct Investment ◽  
Time Series Data ◽  
Series Data ◽  
Balance Of Trade ◽  
The Impact

This study aims at investigating the impact of globalization on economic growth in the case of Vietnam. Empirical analysis is done by using time series data for the period from 1995 to 2014. The paper tested the stationary cointegration of time series data and utilized the error correction modeling technique to determine the short run relationships among economic growth, globalization, foreign direct investment, balance of trade and exchange rate variables. Then, the long run relationship between economic growth and the variables representing economic integration were estimated by ordinary least square. The results show that globalization, measured by the KOF index, promotes economic growth and Vietnam has gained from integrating into the global economy. The overall index of globalization had positively and significantly impacted the economic growth in Vietnam. The results also indicated that economic globalization had a significantly positive effect on economic growth in the period examined. The study further revealed that foreign direct investment and the exchange rate affect economic growth positively whereas balance of trade affects economic growth negatively.

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

scholarly journals Political Stability and its Impact on Economic Growth of Pakistan (1988-2018): A Time Series Analysis

2020 ◽  
Vol 6 (1) ◽  
pp. 273-282
Author(s):  
Majid Hussain Phul ◽  
Muhammad Saleem Rahpoto ◽  
Ghulam Muhammad Mangnejo
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Time Series Data ◽  
Political Stability ◽  
Series Data ◽  
Explanatory Variables ◽  
Long Run ◽  
Short Run ◽  
The Impact ◽  
Positive Effect

This research paper empirically investigates the outcome of Political stability on economic growth (EG) of Pakistan for the period of 1988 to 2018. Political stability (PS), gross fixed capital formation (GFCF), total labor force (TLF) and Inflation (INF) are important explanatory variables. Whereas for model selection GDPr is used as the dependent variable. To check the stationary of time series data Augmented Dickey Fuller (ADF) unit root (UR) test has been used,  and whereas to find out the long run relationship among variables, OLS method has been used. The analysis the impact of PS on EG (EG) in the short run, VAR model has been used. The outcomes show that all the variables (PS, GFCF, TLF and INF) have a significantly positive effect on the EG of Pakistan in the long run period. But the effect of PS on GDP is smaller. Further, in this research we are trying to see the short run relationship between GDP and other explanatory variables. The outcomes show that PS does not have such effect on GDP in the short run analysis. While GFCF, TLF and INF have significantly positive effect on GDP of Pakistan in the short run period.

scholarly journals The Effect of IJ-EPA to Indonesia Export: Interupted Time Series (ITS) Approach

2020 ◽  
Vol 2 (1) ◽  
pp. 128-145
Author(s):  
Yuafanda Kholfi Hartono ◽  
Sumarto Eka Putra
Keyword(s):  
Time Series ◽  
Free Trade ◽  
Free Trade Agreement ◽  
Time Series Data ◽  
Current Account Deficit ◽  
Trade Agreement ◽  
Export Market ◽  
Series Data ◽  
The Government ◽  
The Impact

Indonesia Japan Economic Partnership Agreement (IJ-EPA) is a bilateral free-trade agreement between Indonesia and Japan that has been started from July 1st, 2008. After more than a decade of its implementation, there is a question that we need to be addressed: Does liberalization of IJ-EPA make Indonesia’s export to Japan increase? This question is important since the government gives a trade-off by giving lower tariff for certain commodities agreed in agreement to increase export. Using Interrupted time series (ITS) analysis based on time-series data from Statistics Indonesia (BPS), this article found that the impact of IJ-EPA decreased for Indonesia export to Japan. Furthermore, this paper proposed some potential commodities that can increase the effectiveness of this FTA. The importance of this topic is that Indonesia will maximize the benefit in implementing of agreement that they made from the third biggest destination export of their total export value, so it will be in line with the government's goal to expand export market to solve current account deficit. In addition, the method that used in this paper can be implemented to other countries so that they can maximize the effect of Free Trade Agreement, especially for their export.

scholarly journals Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data

2020 ◽  
Vol 12 (22) ◽  
pp. 3798
Author(s):  
Lei Ma ◽  
Michael Schmitt ◽  
Xiaoxiang Zhu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Series Data ◽  
Spectral Bands ◽  
Object Based ◽  
Spatio Temporal ◽  
Optical Satellite Images ◽  
The Impact

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification.

Sign in / Sign up

Export Citation Format

Share Document